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Prognosis of Heart Disease using Data Mining Techniques: A Comprehensive Survey

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Ankita Naik, Nitesh Naik
10.5120/ijca2018917765

Ankita Naik and Nitesh Naik. Prognosis of Heart Disease using Data Mining Techniques: A Comprehensive Survey. International Journal of Computer Applications 181(17):14-18, September 2018. BibTeX

@article{10.5120/ijca2018917765,
	author = {Ankita Naik and Nitesh Naik},
	title = {Prognosis of Heart Disease using Data Mining Techniques: A Comprehensive Survey},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2018},
	volume = {181},
	number = {17},
	month = {Sep},
	year = {2018},
	issn = {0975-8887},
	pages = {14-18},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume181/number17/29913-2018917765},
	doi = {10.5120/ijca2018917765},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Prediction and diagnosis of heart disease has become a formidable factor faced by medical practitioners and hospitals both in India and also worldwide. The early and timely diagnosis of heart disease plays a very crucial role in halting its advancement and reducing related medical costs. Taking into account the ever-increasing rise in heart disease-induced mortality, different techniques have been adopted to treat it. The idea intends to develop a heart disease prediction model, which will implement ensemble techniques, can help the doctors in detecting the heart disease status based on the patient's clinical data. This paper provides a quick and facile analysis and understanding of available prediction models using data mining from 2011 to 2017. The comparison shows the accuracy level of each model given by different researchers.

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Keywords

Prediction, heart disease, classification, ensemble, diagnosis